Methods and systems for diagnosis and treatment of neural diseases and disorders

Abstract

The invention disclosed herein relates to methods and systems for modulating activity of a nervous system component. Neural pattern recognition is used to identify a neurological and/or psychiatric disease or disorder based on input generated by electric signals indicative of the subject's brain activity. In an embodiment, the method comprises receiving an input from one or more sensors, each sensor configured to sense a particular characteristic indicative of a neurological or psychiatric condition or state; developing treatment parameters based on the input received from the one or more sensors; and generating neural modulation signals for delivery to a nervous system component through one or more output devices in accordance with one or more developed treatment parameters.

Description

FIELD OF THE INVENTION[0001]The invention disclosed herein generally relates to methods and systems for neural pattern recognition in a subject suffering from a neurological and / or psychiatric disease or disorder based on input generated by electric signals indicative of the subject's brain activity.BACKGROUND OF THE INVENTION[0002]A number of techniques have been developed for controlling neurological conditions by electronically stimulating a pre-determined neurological region non-invasively. An example of such a technique is trancranial magnetic stimulation (TMS), which uses the principle of inductance to deliver electrical energy across the scalp and skull without the pain of direct percutaneous electrical stimulation.[0003]In general, these procedures involve identifying a discrete region of the brain and focusing fluctuating magnetic fields generated by one or more coils positioned proximate the head at a location that induces electric current in the identified region of the b...

AI Technical Summary

Benefits of technology

[0011]Thus, in some embodiments, the neurological control system disclosed herein performs automated determination of the optimum magnitude of treatment. By sensing and quantifying the magnitude and frequency of brain activity in the patient, a quantitative representation of the level or “state” of the disease is determined. The disease state is monitored as treatment parameters are automatically varied, and the local or absolute minimum in disease state is achieved as the optimal set of stimulation parameters is converged upon. The disease state can be represented as a single value or a vector or matrix of values; in the latter two cases, a multi variable optimization algorithm can be employed with appropriate weighting factors. Automated optimization of treatment parameters expedites achievement of satisfactory treatment of the patient, reducing the time and number of interactions, such as physician visits, endured by the patient. This optimization includes selection of electrode polarities, electrode configurations stimulating parameter waveforms, temporal profile of stimulation magnitude, stimulation duty cycles, baseline stimulation magnitude, intermittent stimulation magnitude and timing, and other stimulation parameters.

Problems solved by technology

While such treatment modalities offer some benefits to patients, their efficacy is limited.

With such treatment modalities it is difficult and often impossible to arrive at an optimal treatment “magnitude,” that is, an optimal dose or intensity of treatment.

Furthermore, patients are subjected to periods of over-treatment and under-treatment due to variations in disease state.

Moreover, a particularly significant drawback to the above and other traditional treatment modalities is that they suffer from inconsistencies in treatment magnitude.

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